A model for one-dimensional deposition and consolidation of shrinking mine fills
Bibliographic record
Abstract
The consolidation of a cemented mine fill is controlled by the rate of backfilling in regard to its permeability as well as the companion chemical shrinkage due to hydration reaction. A unified equation is derived in the study to delineate the one-dimensional (1D) deposition and consolidation of shrinking mine fills based on a generic description of the entangled chemo–hygro–mechanical processes. The proposed equation has taken into account the compressibility of each phase and the refinement of pore space due to phase exchange of water during hydration, which are lacking in other 1D formulations. The resulting equation would thus allow for more rational and convenient analyses of mine-fill consolidation during accretion and after deposition with a unified model. The improvement is practically at the mere expense of introducing only one additional parameter (the stiffness of solid grains) compared with existing benchmark equations. Parametric analyses performed with the proposed equation have revealed the limitations of the original Gibson and other 1D solutions in realistic assessment of mine-fill consolidation due to the lack of considerations for internal couplings. The proposed equation could also provide more reliable benchmark solutions for verification of complex numerical tools that incorporate coupled modelling of mine-fill behaviours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".